Design of Direct Adaptive Controller for a Half-Car Suspension System

被引：0

作者：

Pedro, Jimoh O. ^{[1
]}

Baloyi, Nyiko ^{[1
]}

机构：

[1] Univ Witwatersrand, Sch Mech Aeronaut & Ind Engn, Private Bag 03, ZA-2050 Johannesburg, South Africa

来源：

2017 IEEE AFRICON | 2017年

关键词：

PID control; nonlinear half-car model; active vehicle suspension system; direct adaptive neural control; feedback linearisation; radial basis function neural network; ACTIVE SUSPENSIONS; VEHICLE; COMFORT; FUZZY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A direct adaptive neural network (DANN) controller is designed for a four degree of freedom (4-DOF) nonlinear halfcar suspension system. A radial basis function neural network (RBFNN) is used as part of the feedback linearisation (FBL) control law to make the controller adaptive by adjusting the network weights online. Performance of the DANN controller is benchmarked against that of a passive vehicle suspension system (PVSS) and of a fixed-gain PID controller. The active vehicle suspension system (AVSS) is subjected to a deterministic road disturbance during controller design. The novelty in this work is the design and application of a DANN controller to improve the ride comfort and road holding performance of the AVSS.

引用

页码：467 / 472

页数：6

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